Mingxing Tan | AutoML for Efficient Vision Learning
Details
Virtual London Machine Learning Meetup - 08.03.2021 @ 18:30
We would like to invite you to our next Virtual Machine Learning Meetup. Please read the papers below and help us create a vibrant discussion.
Agenda:
- 18:25: Virtual doors open
- 18:30: Talk
- 19:10: Q&A session
- 19:30: Close
Sponsors
https://evolution.ai/ : Machines that Read - Intelligent data extraction from corporate and financial documents.
- Title: AutoML for Efficient Vision Learning (Mingxing Tan is a researcher at Google Brain)
- Papers:
EfficientNet - https://arxiv.org/abs/1905.11946
EfficientDet - https://arxiv.org/abs/1911.09070
MnasNet - https://arxiv.org/abs/1807.11626
Abstract: This talk will focus on a few recent progresses we have made on AutoML, particularly on neural architecture search for efficient convolutional neural networks. We will first discuss the challenges and solutions in designing network architecture search spaces / algorithms / constraints, as well as hyperparamter auto-tuning. Afterwards, we will discuss how to scale up neural networks for better accuracy and efficiency. We will conclude the talk with a few AutoML applications on image classification, detection, segmentation.
Bio: Mingxing Tan is a researcher at Google Brain, mainly focusing on AutoML research and applications. He has co-authored several popular models including EfficientNet and EfficientDet. He finished his Ph.D. at Peking University and Post-Doc at Cornell University.
